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State Space Modeling of Time Series

  • Book
  • © 1990

Overview

Authors:
  1. Masanao Aoki

    1. Department of Computer Science, University of California, Los Angeles, USA

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Part of the book series: Universitext (UTX)

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Table of contents (12 chapters)

  1. Front Matter

    Pages I-XVII
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  2. Introduction

    • Masanao Aoki
    Pages 1-2

  3. The Notion of State

    • Masanao Aoki
    Pages 3-7

  4. Data Generating Processes

    • Masanao Aoki
    Pages 8-20

  5. State Space and ARMA Models

    • Masanao Aoki
    Pages 21-38

  6. Properties of State Space Models

    • Masanao Aoki
    Pages 39-49

  7. Hankel Matrix and Singular Value Decomposition

    • Masanao Aoki
    Pages 50-70

  8. Innovation Models, Riccati Equations, and Multiplier Analysis

    • Masanao Aoki
    Pages 71-98

  9. State Vectors and Optimality Measures

    • Masanao Aoki
    Pages 99-104

  10. Estimation of System Matrices

    • Masanao Aoki
    Pages 105-164

  11. Approximate Models and Error Analysis

    • Masanao Aoki
    Pages 165-186

  12. Integrated Time Series

    • Masanao Aoki
    Pages 187-228

  13. Numerical Examples

    • Masanao Aoki
    Pages 229-248

  14. Back Matter

    Pages 249-326
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Keywords

About this book

In this book, the author adopts a state space approach to time series modeling to provide a new, computer-oriented method for building models for vector-valued time series. This second edition has been completely reorganized and rewritten. Background material leading up to the two types of estimators of the state space models is collected and presented coherently in four consecutive chapters. New, fuller descriptions are given of state space models for autoregressive models commonly used in the econometric and statistical literature. Backward innovation models are newly introduced in this edition in addition to the forward innovation models, and both are used to construct instrumental variable estimators for the model matrices. Further new items in this edition include statistical properties of the two types of estimators, more details on multiplier analysis and identification of structural models using estimated models, incorporation of exogenous signals and choice of model size. A whole new chapter is devoted to modeling of integrated, nearly integrated and co-integrated time series.

Authors and Affiliations

  • Department of Computer Science, University of California, Los Angeles, USA

    Masanao Aoki

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